Coursera
Building AI Agents for Complex Tasks

Enjoy unlimited growth with a year of Coursera Plus for $199 (regularly $399). Save now.

Coursera

Building AI Agents for Complex Tasks

Hurix Digital

Instructor: Hurix Digital

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

December 2025

Assessments

7 assignments¹

AI Graded see disclaimer
Taught in English

See how employees at top companies are mastering in-demand skills

 logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 3 modules in this course

This foundational lesson introduces what AI agents are and how they differ from traditional software. Learners will explore agent-environment interactions, the concept of perception, and how various types of agents—reactive, deliberative, and hybrid—handle decision-making. Through real-world examples like smart assistants and warehouse robots, learners will classify agent types and determine where each model excels or breaks down.

What's included

4 videos1 reading1 assignment

This lesson moves from theory to implementation. Learners will construct intelligent agents that integrate inputs (perception), structured reasoning (decision loops), and output (action). They'll explore core modules such as memory, planning chains, and tool execution in LangChain and Rasa. Real-world examples like Alexa’s task-based updates and LangChain agents with tools will help frame the technical walkthroughs.

What's included

3 videos1 reading2 assignments

In the final lesson, learners will focus on evaluating how agents perform in realistic, changing environments. They'll explore testing strategies, interpret edge-case behaviors, and fine-tune agents using logs, performance feedback, and outcome tracking. Examples such as AlphaCode’s reasoning iterations and BabyAGI’s task queue refinement will help frame the concepts. This lesson culminates in the Capstone project, where learners will apply everything they've learned to design and deliver an intelligent, goal-driven agent.

What's included

4 videos1 reading4 assignments

Instructor

Hurix Digital
Coursera
56 Courses1,978 learners

Offered by

Coursera

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Coursera Plus

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.